Sustainability transition research seeks to understand the patterns and dynamics of structural societal change as well as unearth strategies for governance. However, existing frameworks emphasize innovation and build-up over exnovation and break-down. This limits their potential in making sense of the turbulent and chaotic dynamics of current transition-in-the-making. Addressing this gap, our paper elaborates on the development and use of the X-curve framework. The X-curve provides a simplified depiction of transitions that explicitly captures the patterns of build-up, breakdown, and their interactions.Using three cases, we illustrate the X-curve’s main strength as a framework that can support groups of people to develop a shared understanding of the dynamics in transitions-in-the-making. This helps them reflect upon their roles, potential influence, and the needed capacities for desired transitions. We discuss some challenges in using the X-curve framework, such as participants’ grasp of ‘chaos’, and provide suggestions on how to address these challenges and strengthen the frameworks’ ability to support understanding and navigation of transition dynamics. We conclude by summarizing its main strength and invite the reader to use it, reflect on it, build on it, and judge its value for action research on sustainability transitions themselves.
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Abstract Aims: To lower the threshold for applying ultrasound (US) guidance during peripheral intravenous cannulation, nurses need to be trained and gain experience in using this technique. The primary outcome was to quantify the number of procedures novices require to perform before competency in US-guided peripheral intravenous cannulation was achieved. Materials and methods: A multicenter prospective observational study, divided into two phases after a theoretical training session: a handson training session and a supervised life-case training session. The number of US-guided peripheral intravenous cannulations a participant needed to perform in the life-case setting to become competent was the outcome of interest. Cusum analysis was used to determine the learning curve of each individual participant. Results: Forty-nine practitioners participated and performed 1855 procedures. First attempt cannulation success was 73% during the first procedure, but increased to 98% on the fortieth attempt (p<0.001). The overall first attempt success rate during this study was 93%. The cusum learning curve for each practitioner showed that a mean number of 34 procedures was required to achieve competency. Time needed to perform a procedure successfully decreased when more experience was achieved by the practitioner, from 14±3 minutes on first procedure to 3±1 minutes during the fortieth procedure (p<0.001). Conclusions: Competency in US-guided peripheral intravenous cannulation can be gained after following a fixed educational curriculum, resulting in an increased first attempt cannulation success as the number of performed procedures increased.
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Background: Modern modeling techniques may potentially provide more accurate predictions of dichotomous outcomes than classical techniques. Objective: In this study, we aimed to examine the predictive performance of eight modeling techniques to predict mortality by frailty. Methods: We performed a longitudinal study with a 7-year follow-up. The sample consisted of 479 Dutch community-dwelling people, aged 75 years and older. Frailty was assessed with the Tilburg Frailty Indicator (TFI), a self-report questionnaire. This questionnaire consists of eight physical, four psychological, and three social frailty components. The municipality of Roosendaal, a city in the Netherlands, provided the mortality dates. We compared modeling techniques, such as support vector machine (SVM), neural network (NN), random forest, and least absolute shrinkage and selection operator, as well as classical techniques, such as logistic regression, two Bayesian networks, and recursive partitioning (RP). The area under the receiver operating characteristic curve (AUROC) indicated the performance of the models. The models were validated using bootstrapping. Results: We found that the NN model had the best validated performance (AUROC=0.812), followed by the SVM model (AUROC=0.705). The other models had validated AUROC values below 0.700. The RP model had the lowest validated AUROC (0.605). The NN model had the highest optimism (0.156). The predictor variable “difficulty in walking” was important for all models. Conclusions: Because of the high optimism of the NN model, we prefer the SVM model for predicting mortality among community-dwelling older people using the TFI, with the addition of “gender” and “age” variables. External validation is a necessary step before applying the prediction models in a new setting.
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